1. Import Libraries

%autosave 300

Autosaving every 300 seconds

#Print all the outputs in a cell
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"

import pandas as pd
import numpy as np
import seaborn as sns
%pylab inline

import sklearn as sk
import sklearn.tree
from IPython.display import Image  
import pydotplus
import plotly.express as px
import sklearn.tree as tree

#Suppress Pandas future warning 
import warnings
warnings.filterwarnings('ignore')

Populating the interactive namespace from numpy and matplotlib

pd.set_option('display.max_columns',None)

2. Load Dataset and Exploratory Data Analysis

df = pd.read_csv('Telco_Data_V3.csv')

df.head()

	LoyaltyID	Customer ID	Senior Citizen	Partner	Dependents	Tenure	Phone Service	Multiple Lines	Internet Service	Online Security	Online Backup	Device Protection	Tech Support	Streaming TV	Streaming Movies	Contract	Paperless Billing	Payment Method	Monthly Charges	Total Charges	Gender	Age	Under 30	Senior Citizen.1	Married	Dependents.1	Number of Dependents	City	Zip Code	Latitude	Longitude	Population	Total Revenue	Satisfaction Score	Customer Status	Churn Value	Churn Score	Churn Category	Churn Reason
0	318537	7590-VHVEG	No	Yes	No	1	No	No phone service	DSL	No	Yes	No	No	No	No	Month-to-month	Yes	Electronic check	29.85	29.85	Female	36	No	No	Yes	No	0	Los Angeles	90001	33.973616	-118.249020	54492	29.85	3	Joined	0	27	0	0
1	152148	5575-GNVDE	No	No	No	34	Yes	No	DSL	Yes	No	Yes	No	No	No	One year	No	Mailed check	56.95	1889.5	Male	46	No	No	No	No	0	Los Angeles	90002	33.949255	-118.246978	44586	2470.56	3	Stayed	0	61	0	0
2	326527	3668-QPYBK	No	No	No	2	Yes	No	DSL	Yes	Yes	No	No	No	No	Month-to-month	Yes	Mailed check	53.85	108.15	Male	37	No	No	No	No	0	Los Angeles	90003	33.964131	-118.272783	58198	129.09	1	Churned	1	86	Competitor	Competitor made better offer
3	845894	7795-CFOCW	No	No	No	45	No	No phone service	DSL	Yes	No	Yes	Yes	No	No	One year	No	Bank transfer (automatic)	42.30	1840.75	Male	53	No	No	No	No	0	Los Angeles	90004	34.076259	-118.310715	67852	1840.75	3	Stayed	0	66	0	0
4	503388	9237-HQITU	No	No	No	2	Yes	No	Fiber optic	No	No	No	No	No	No	Month-to-month	Yes	Electronic check	70.70	151.65	Female	19	Yes	No	No	Yes	2	Los Angeles	90005	34.059281	-118.307420	43019	169.89	2	Churned	1	67	Other	Moved

df.corr()

	LoyaltyID	Tenure	Monthly Charges	Age	Number of Dependents	Zip Code	Latitude	Longitude	Population	Total Revenue	Satisfaction Score	Churn Value	Churn Score
LoyaltyID	1.000000	-0.003633	-0.006984	-0.018213	0.005464	-0.022660	-0.030903	0.027215	0.020175	-0.007301	-0.000186	0.009513	0.013237
Tenure	-0.003633	1.000000	0.247900	0.010202	0.107689	0.007362	0.012075	-0.009716	-0.012388	0.853078	0.210056	-0.352229	-0.224523
Monthly Charges	-0.006984	0.247900	1.000000	0.144685	-0.131837	-0.010775	-0.030325	0.028429	0.009597	0.588887	-0.223922	0.193356	0.132115
Age	-0.018213	0.010202	0.144685	1.000000	-0.119000	-0.008183	-0.010305	0.007612	-0.018881	0.048265	-0.085170	0.115760	0.083919
Number of Dependents	0.005464	0.107689	-0.131837	-0.119000	1.000000	0.016493	0.029081	-0.024271	-0.015486	0.038038	0.152873	-0.218780	-0.160743
Zip Code	-0.022660	0.007362	-0.010775	-0.008183	0.016493	1.000000	0.894769	-0.790564	-0.424067	0.002944	0.002121	-0.016289	-0.018020
Latitude	-0.030903	0.012075	-0.030325	-0.010305	0.029081	0.894769	1.000000	-0.885979	-0.434801	0.000082	0.022367	-0.041546	-0.037615
Longitude	0.027215	-0.009716	0.028429	0.007612	-0.024271	-0.790564	-0.885979	1.000000	0.296288	0.001062	-0.009048	0.024052	0.019394
Population	0.020175	-0.012388	0.009597	-0.018881	-0.015486	-0.424067	-0.434801	0.296288	1.000000	-0.010546	-0.031056	0.051649	0.020012
Total Revenue	-0.007301	0.853078	0.588887	0.048265	0.038038	0.002944	0.000082	0.001062	-0.010546	1.000000	0.094796	-0.223003	-0.138072
Satisfaction Score	-0.000186	0.210056	-0.223922	-0.085170	0.152873	0.002121	0.022367	-0.009048	-0.031056	0.094796	1.000000	-0.754649	-0.495144
Churn Value	0.009513	-0.352229	0.193356	0.115760	-0.218780	-0.016289	-0.041546	0.024052	0.051649	-0.223003	-0.754649	1.000000	0.660772
Churn Score	0.013237	-0.224523	0.132115	0.083919	-0.160743	-0.018020	-0.037615	0.019394	0.020012	-0.138072	-0.495144	0.660772	1.000000

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7043 entries, 0 to 7042
Data columns (total 39 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   LoyaltyID             7043 non-null   int64  
 1   Customer ID           7043 non-null   object 
 2   Senior Citizen        7043 non-null   object 
 3   Partner               7043 non-null   object 
 4   Dependents            7043 non-null   object 
 5   Tenure                7043 non-null   int64  
 6   Phone Service         7043 non-null   object 
 7   Multiple Lines        7043 non-null   object 
 8   Internet Service      7043 non-null   object 
 9   Online Security       7043 non-null   object 
 10  Online Backup         7043 non-null   object 
 11  Device Protection     7043 non-null   object 
 12  Tech Support          7043 non-null   object 
 13  Streaming TV          7043 non-null   object 
 14  Streaming Movies      7043 non-null   object 
 15  Contract              7043 non-null   object 
 16  Paperless Billing     7043 non-null   object 
 17  Payment Method        7043 non-null   object 
 18  Monthly Charges       7043 non-null   float64
 19  Total Charges         7043 non-null   object 
 20  Gender                7043 non-null   object 
 21  Age                   7043 non-null   int64  
 22  Under 30              7043 non-null   object 
 23  Senior Citizen.1      7043 non-null   object 
 24  Married               7043 non-null   object 
 25  Dependents.1          7043 non-null   object 
 26  Number of Dependents  7043 non-null   int64  
 27  City                  7043 non-null   object 
 28  Zip Code              7043 non-null   int64  
 29  Latitude              7043 non-null   float64
 30  Longitude             7043 non-null   float64
 31  Population            7043 non-null   int64  
 32  Total Revenue         7043 non-null   float64
 33  Satisfaction Score    7043 non-null   int64  
 34  Customer Status       7043 non-null   object 
 35  Churn Value           7043 non-null   int64  
 36  Churn Score           7043 non-null   int64  
 37  Churn Category        7043 non-null   object 
 38  Churn Reason          7043 non-null   object 
dtypes: float64(4), int64(9), object(26)
memory usage: 2.1+ MB

3. Basic Data Cleaning

df1 = df.copy()

#Replacing strings with numbers 1 or 0 
df1.replace({'Yes':1,'No':0,'No phone service':0,'No internet service':0},inplace = True)

#Drop duplicate columns or columns that are not useful for our analysis
df1.drop(columns = ['LoyaltyID','Customer ID','Senior Citizen.1','Married','Dependents.1','Customer Status'],inplace = True)

df1.head(1)

	Senior Citizen	Partner	Dependents	Tenure	Phone Service	Multiple Lines	Internet Service	Online Security	Online Backup	Device Protection	Tech Support	Streaming TV	Streaming Movies	Contract	Paperless Billing	Payment Method	Monthly Charges	Total Charges	Gender	Age	Under 30	Number of Dependents	City	Zip Code	Latitude	Longitude	Population	Total Revenue	Satisfaction Score	Churn Value	Churn Score	Churn Category	Churn Reason
0	0	1	0	1	0	0	DSL	0	1	0	0	0	0	Month-to-month	1	Electronic check	29.85	29.85	Female	36	0	0	Los Angeles	90001	33.973616	-118.24902	54492	29.85	3	0	27	0	0

df1.isna().sum()

Senior Citizen          0
Partner                 0
Dependents              0
Tenure                  0
Phone Service           0
Multiple Lines          0
Internet Service        0
Online Security         0
Online Backup           0
Device Protection       0
Tech Support            0
Streaming TV            0
Streaming Movies        0
Contract                0
Paperless Billing       0
Payment Method          0
Monthly Charges         0
Total Charges           0
Gender                  0
Age                     0
Under 30                0
Number of Dependents    0
City                    0
Zip Code                0
Latitude                0
Longitude               0
Population              0
Total Revenue           0
Satisfaction Score      0
Churn Value             0
Churn Score             0
Churn Category          0
Churn Reason            0
dtype: int64

df1.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 7043 entries, 0 to 7042
Data columns (total 33 columns):
 #   Column                Non-Null Count  Dtype  
---  ------                --------------  -----  
 0   Senior Citizen        7043 non-null   int64  
 1   Partner               7043 non-null   int64  
 2   Dependents            7043 non-null   int64  
 3   Tenure                7043 non-null   int64  
 4   Phone Service         7043 non-null   int64  
 5   Multiple Lines        7043 non-null   int64  
 6   Internet Service      7043 non-null   object 
 7   Online Security       7043 non-null   int64  
 8   Online Backup         7043 non-null   int64  
 9   Device Protection     7043 non-null   int64  
 10  Tech Support          7043 non-null   int64  
 11  Streaming TV          7043 non-null   int64  
 12  Streaming Movies      7043 non-null   int64  
 13  Contract              7043 non-null   object 
 14  Paperless Billing     7043 non-null   int64  
 15  Payment Method        7043 non-null   object 
 16  Monthly Charges       7043 non-null   float64
 17  Total Charges         7043 non-null   object 
 18  Gender                7043 non-null   object 
 19  Age                   7043 non-null   int64  
 20  Under 30              7043 non-null   int64  
 21  Number of Dependents  7043 non-null   int64  
 22  City                  7043 non-null   object 
 23  Zip Code              7043 non-null   int64  
 24  Latitude              7043 non-null   float64
 25  Longitude             7043 non-null   float64
 26  Population            7043 non-null   int64  
 27  Total Revenue         7043 non-null   float64
 28  Satisfaction Score    7043 non-null   int64  
 29  Churn Value           7043 non-null   int64  
 30  Churn Score           7043 non-null   int64  
 31  Churn Category        7043 non-null   object 
 32  Churn Reason          7043 non-null   object 
dtypes: float64(4), int64(21), object(8)
memory usage: 1.8+ MB

#Convert object type to numeric value
df1['Total Charges'] = pd.to_numeric(df1['Total Charges'], errors='coerce')

#Calculate the churn rate based on each zip code
df_churn_rate = pd.DataFrame(df1.groupby('Zip Code')['Churn Value'].mean()).\
rename(columns = {'Churn Value':'churn_rate'})
#Df_churn_rate.head(1)

#Merging in zip code so we can view churn rate by customer by zip code
df1 = df1.merge(df_churn_rate, left_on = 'Zip Code', right_on= 'Zip Code',how='left')
#df1.head(1)

df1 = pd.get_dummies(df1, columns=['Internet Service','Contract'],dummy_na=False)

df2 = df1.copy()
df2 = pd.get_dummies(df2, columns=['Payment Method','Gender','Churn Reason'])
df_churn = df2[df2['Churn Value'] == 1]
#df_churn.head(1)

df3 = df1.copy()
df3 = pd.get_dummies(df3, columns=['City','Payment Method','Gender'])

df1.head(1)

	Senior Citizen	Partner	Dependents	Tenure	Phone Service	Multiple Lines	Online Security	Online Backup	Device Protection	Tech Support	Streaming TV	Streaming Movies	Paperless Billing	Payment Method	Monthly Charges	Total Charges	Gender	Age	Under 30	Number of Dependents	City	Zip Code	Latitude	Longitude	Population	Total Revenue	Satisfaction Score	Churn Value	Churn Score	Churn Category	Churn Reason	churn_rate	Internet Service_0	Internet Service_DSL	Internet Service_Fiber optic	Contract_Month-to-month	Contract_One year	Contract_Two year
0	0	1	0	1	0	0	0	1	0	0	0	0	1	Electronic check	29.85	29.85	Female	36	0	0	Los Angeles	90001	33.973616	-118.24902	54492	29.85	3	0	27	0	0	0.25	0	1	0	1	0	0

#Dropping N/As from our dataset given small value
df3.isna().any().sum()

1

df3.dropna(inplace=True)
len(df3)

7032

4. Dataset Description

https://community.ibm.com/accelerators/?context=analytics&query=telco%20customer&type=Data

This dataset was sourced from IBM and contains customer information from a Telecommunications company (Telco) that provides a variety of phone and internet services in California. Each row represents a customer and the columns provide different attributes of each customer such as demographic information, the type of services they are enrolled in, and whether or not they churned (i.e. left the company).

• 7,043 Observations
  
  
• 39 Columns in total
  
  

**Variables of Consideration**

1. Churn_Value: Binary - 1 if customer churns, 0 if not
   
   
2. Senior_Citizen: Binary - 1 if senior citizen, 0 if not
   
   
3. Tech_Support: Binary - 'Yes' if customer has tech support, 'No' if not
   
   
4. Internet_Service: Categorical - Type of internet service customer has (Fiber Optic, DSL, No internet service)
   
   
5. Contract: Categorical - Customer contract types (month-to-month, one year, two year)
   
   
6. Churn_Reason: Categorical - Reason why customer churned (ex. Competitor had better devices)
   
   
7. City: Categorical - City customer resides in
   
   
8. Population: Continuous - Population of city customer resides in
   
   
9. Latitude: Continuous - Latitude of city customer resides in
   
   
10. Monthly Charges: Continuous - Total monthly charges per customer

5. Dataset Findings

Given Telco is observing higher churn rates than the industry benchmark (27% vs 22%), our overall goal is to make recommendations as to how the company can improve churn rates, which will help improve customer retention and mitigate revenue loss.

Finding 1: Contract_Month-to-month and Internet Service_Fiber optic are key drivers as to why customers churn.

How did we get to our finding?

Run ML Decision Tree to find patterns in data

dt = tree.DecisionTreeClassifier(max_depth=2)

#Removing all highly correlated columns to 'Churn Value'
X = df3.drop(columns=['Churn Value','Satisfaction Score','Churn Reason',\
                      'Churn Category','Churn Score','churn_rate'])

Y = df3['Churn Value']

dt.fit(X,Y)

DecisionTreeClassifier(max_depth=2)

dt_feature_names = list(X.columns)
dt_target_names = [str(s) for s in Y.unique()]
tree.export_graphviz(dt, out_file='tree.dot', 
    feature_names=dt_feature_names, class_names=dt_target_names,
    filled=True)  
graph = pydotplus.graph_from_dot_file('tree.dot')
Image(graph.create_png())

The decision tree pointed to Contract_Month-to-Month, Internet Service_Fiber optic, and Monthly Charges. We explored those variables in tables and plots below to validate the tree's findings.

#Churn by Internet Service Type which indicates Fiber Optic has definitively higher churn than DSL.
sns.countplot(x='Churn Value',hue='Internet Service',data=df)

<AxesSubplot:xlabel='Churn Value', ylabel='count'>

#Churn by Contract Type which indicates Month-to-Month contracts have a definitively higher churn rate than longer term contracts.
sns.countplot(x='Churn Value',hue='Contract',data=df)

<AxesSubplot:xlabel='Churn Value', ylabel='count'>

df.head(1)

	LoyaltyID	Customer ID	Senior Citizen	Partner	Dependents	Tenure	Phone Service	Multiple Lines	Internet Service	Online Security	Online Backup	Device Protection	Tech Support	Streaming TV	Streaming Movies	Contract	Paperless Billing	Payment Method	Monthly Charges	Total Charges	Gender	Age	Under 30	Senior Citizen.1	Married	Dependents.1	Number of Dependents	City	Zip Code	Latitude	Longitude	Population	Total Revenue	Satisfaction Score	Customer Status	Churn Value	Churn Score	Churn Category	Churn Reason
0	318537	7590-VHVEG	No	Yes	No	1	No	No phone service	DSL	No	Yes	No	No	No	No	Month-to-month	Yes	Electronic check	29.85	29.85	Female	36	No	No	Yes	No	0	Los Angeles	90001	33.973616	-118.24902	54492	29.85	3	Joined	0	27	0	0

df4=df2.copy()

#Group by Churn Value, Month-to-Month contracts, Monthly Charges, and Satisfaction Score which confirm the results 
#of our original decision tree.
df4.groupby(['Churn Value','Contract_Month-to-month']).agg({'Internet Service_Fiber optic':'mean',\
                            'Monthly Charges':'mean','Satisfaction Score':'mean'}).style.background_gradient(cmap = 'Blues')

		Internet Service_Fiber optic	Monthly Charges	Satisfaction Score
Churn Value	Contract_Month-to-month
0	0	0.281991	61.116689	3.746445
	1	0.435135	61.462635	3.847748
1	0	0.630841	85.438084	1.906542
	1	0.702115	73.019396	1.714199

#Groupby of Churn Value which shows of those customers who churned, a greater proportion of them
#were Fiber Optic Customers, had month-to-month contracts, and a lower satisfaction score. 
df4.groupby('Churn Value').agg({'Contract_Month-to-month':'mean','Internet Service_Fiber optic':'mean','Monthly Charges':'mean',\
                                            'Satisfaction Score':'mean'})

	Contract_Month-to-month	Internet Service_Fiber optic	Monthly Charges	Satisfaction Score
Churn Value
0	0.429068	0.347700	61.265124	3.789911
1	0.885500	0.693954	74.441332	1.736223

#The average satisfaction score is consistently lower for customers who have monthly charges above $94/month.
#Almost the entirety of customers who pay more than $94/month subscribe to Fiber Optic.
df4['Mcharge_bin'] = pd.cut(df4['Monthly Charges'], bins=[0,93.68,119])
df4.groupby(['Churn Value','Contract_Month-to-month','Mcharge_bin']).agg({'Internet Service_Fiber optic':'mean',\
                            'Monthly Charges':'mean','Satisfaction Score':'mean'}).style.background_gradient(cmap = 'Blues')

			Internet Service_Fiber optic	Monthly Charges	Satisfaction Score
Churn Value	Contract_Month-to-month	Mcharge_bin
0	0	(0.0, 93.68]	0.076253	48.461917	3.765577
		(93.68, 119.0]	0.998483	105.187557	3.679818
	1	(0.0, 93.68]	0.340000	54.865868	3.859474
		(93.68, 119.0]	1.000000	100.630937	3.778125
1	0	(0.0, 93.68]	0.168421	61.501053	2.010526
		(93.68, 119.0]	1.000000	104.547479	1.823529
	1	(0.0, 93.68]	0.614241	65.150469	1.741002
		(93.68, 119.0]	1.000000	99.694430	1.623342

Mid-range Goal - Based off the results from our Decision Tree, the primary reason customers will churn is if they have month-to-month contracts and subscribed to the Fiber Optic internet service. While month-to-month customers tend to churn more often due to lack of contractual obligation, having such a large proportion of customers churn off Fiber Optic indicates an issue with this product whether it be pricing, performance, or competitiveness with other service providers. Our recommendation to management is to work with product teams to investigate whether Fiber Optic quality and performance is meeting industry standards. We seek to explore more specific issues with Fiber Optic in Finding #2 to make more specific recommendations.

Finding 2: Dividing the service area into 4 bins based on latitude allowed us to identify thematic churn reasons for each binned region. We learned that by cutting the service area into different territories, there were different, prevalent issues occuring in each territory.

How did we get to our finding?

We tried to find patterns of churn, by churn categories

#Group Churn Reasons with highest on top
df1[df1['Churn Reason']!='0'].groupby('Churn Reason').agg({'Churn Value':'size'}).\
nlargest(20, 'Churn Value').style.background_gradient(cmap = 'Blues')

	Churn Value
Churn Reason
Competitor had better devices	313
Competitor made better offer	311
Attitude of support person	220
Don't know	130
Competitor offered more data	117
Competitor offered higher download speeds	100
Attitude of service provider	94
Price too high	78
Product dissatisfaction	77
Network reliability	72
Long distance charges	64
Service dissatisfaction	63
Moved	46
Extra data charges	39
Limited range of services	37
Poor expertise of online support	31
Lack of affordable download/upload speed	30
Lack of self-service on Website	29
Poor expertise of phone support	12
Deceased	6

Then used regression tree to look for areas with highest churn.

#Drop the columns that have similar information as other columns; location information (since we can eyeball location from the map); 
#and columns that are highly correlated with churn rate.
X= df_churn.drop(columns = ['Paperless Billing','Total Charges','Under 30','Latitude','Longitude','City','Zip Code',\
'Satisfaction Score','Churn Value','Churn Score','Churn Category','churn_rate'])
Y = df_churn['churn_rate']

dt = sklearn.tree.DecisionTreeRegressor(max_depth = 2)
dt.fit(X,Y)
dt_feature_names = list(X.columns)
dt_target_names = np.array(Y.unique(),dtype=np.string_) 
tree.export_graphviz(dt, out_file='tree.dot', 
    feature_names=dt_feature_names, class_names=dt_target_names,
    filled=True)  
graph = pydotplus.graph_from_dot_file('tree.dot')
Image(graph.create_png())

DecisionTreeRegressor(max_depth=2)

#Plot churn rates by population size bin to confirm Decision Tree results.
df_churn['Population_bin'] = pd.cut(df_churn['Population'],bins=[0,27249,110000])
df_churn.groupby('Population_bin')['churn_rate'].mean().plot(kind='bar')

<AxesSubplot:xlabel='Population_bin'>

Regression indicates high churn rate occurs in the area that has population greater than 27,249 which leads us to explore areas with the most churn.

#Groupby to get zip codes with highest churn rates
ChurnBy_zip1 = df1.groupby(['Zip Code']).agg({'Churn Value':'size','Latitude':'mean',\
                                            'Longitude':'mean','churn_rate':'mean',})
ChurnBy_zip2 = df1.groupby(['Zip Code']).agg({'Churn Value':'size','Latitude':'mean',\
                                            'Longitude':'mean','churn_rate':'mean',}).nlargest(40, 'churn_rate')

Find areas with highest churn count and map them; the dot size represents count of churned customers.

#Map by highest churn quantity by Zip Code, color by churn rate and size by quantity of customers that churned
import plotly.express as px
px.set_mapbox_access_token ='pk.eyJ1IjoiYm1hcmluMiIsImEiOiJja3dicGplOTIydm54Mm9tbnQ2cWFwejV6In0.4VaP0Cmn09tQ07XOlwI_ZQ'
fig = px.scatter_mapbox(data_frame=ChurnBy_zip1, 
                        lat="Latitude", 
                        lon="Longitude",
                        color="churn_rate",
                        size = "Churn Value",
                        color_continuous_scale='rdpu') 

fig.update_layout(mapbox_style="carto-positron",\
                  mapbox_zoom=4.4, mapbox_center = {"lat": 37.5, "lon": -117})

Drill into zip codes with highest churn rates, top 40

#Map with top churn ratios by Zip Code, color by churn rate and size by count of customers 
px.set_mapbox_access_token ='pk.eyJ1IjoiYm1hcmluMiIsImEiOiJja3dicGplOTIydm54Mm9tbnQ2cWFwejV6In0.4VaP0Cmn09tQ07XOlwI_ZQ'
fig = px.scatter_mapbox(data_frame=ChurnBy_zip2, 
                        lat="Latitude", 
                        lon="Longitude",
                        color="churn_rate",
                        size = "Churn Value",
                        color_continuous_scale='turbo') 

fig.update_layout(mapbox_style="carto-positron",\
                  mapbox_zoom=4.4, mapbox_center = {"lat": 37.5, "lon": -117})

Using the maps we decided to bin the service area by latitude to find patterns by region.

df1['geo_bin'] = pd.cut(df1.Latitude, bins=[30,33,35,37,42])

sns.catplot(hue='Churn Reason',y='geo_bin', data=df1[df1['Churn Category']!='0'],\
              palette="Set2", kind='count',aspect=2)

<seaborn.axisgrid.FacetGrid at 0x7fe9ccc2f040>

Use "Churn Reason" to get more granular and find specific patterns

pd.set_option('display.max_rows', 80)
df1[df1['Churn Reason']!='0'].groupby(['geo_bin','Churn Reason']).agg({'Churn Value':'size',\
                                'Internet Service_Fiber optic':'mean','Senior Citizen':'mean',\
                                'Contract_Month-to-month':'mean', 'Tenure':'mean'}).sort_values(ascending=False,\
                                    by=['geo_bin','Churn Value']).rename(columns={'Customer ID':'# Churned',\
                                        'Internet Service_Fiber optic':'Fiber %',\
                                            'Senior Citizen':'Senior %','Contract_Month-to-month':'Contract_M2M %'})

		Churn Value	Fiber %	Senior %	Contract_M2M %	Tenure
geo_bin	Churn Reason
(37, 42]	Competitor had better devices	151	0.768212	0.304636	0.933775	14.841060
	Competitor offered more data	73	0.794521	0.164384	0.835616	25.095890
	Attitude of support person	69	0.681159	0.463768	0.942029	18.507246
	Competitor made better offer	67	0.731343	0.328358	0.925373	14.298507
	Competitor offered higher download speeds	57	0.719298	0.087719	0.842105	20.614035
	Don't know	52	0.750000	0.288462	0.884615	19.153846
	Price too high	43	0.651163	0.279070	0.813953	22.279070
	Network reliability	39	0.564103	0.256410	0.871795	17.282051
	Product dissatisfaction	34	0.705882	0.352941	0.764706	23.911765
	Service dissatisfaction	34	0.735294	0.176471	0.941176	20.735294
	Long distance charges	33	0.393939	0.060606	0.818182	13.060606
	Attitude of service provider	30	0.633333	0.266667	0.866667	20.333333
	Moved	22	0.681818	0.090909	0.818182	16.772727
	Limited range of services	19	0.736842	0.263158	0.894737	13.789474
	Extra data charges	18	0.722222	0.277778	1.000000	21.722222
	Poor expertise of online support	17	0.705882	0.294118	0.823529	15.411765
	Lack of affordable download/upload speed	14	0.785714	0.142857	0.857143	21.285714
	Lack of self-service on Website	13	0.692308	0.000000	0.923077	14.000000
	Poor expertise of phone support	7	0.571429	0.000000	0.714286	15.571429
	Deceased	3	0.666667	0.333333	1.000000	9.333333
(35, 37]	Competitor had better devices	35	0.742857	0.200000	0.942857	12.771429
	Attitude of support person	28	0.607143	0.250000	0.892857	14.642857
	Attitude of service provider	25	0.720000	0.160000	0.760000	19.520000
	Competitor made better offer	12	0.666667	0.500000	0.833333	11.833333
	Don't know	11	0.818182	0.090909	1.000000	14.636364
	Competitor offered higher download speeds	9	0.666667	0.222222	0.777778	17.222222
	Product dissatisfaction	7	0.571429	0.428571	0.857143	25.142857
	Competitor offered more data	6	1.000000	0.166667	0.833333	14.666667
	Long distance charges	6	0.333333	0.000000	0.833333	19.000000
	Moved	5	1.000000	0.400000	0.600000	36.200000
	Price too high	5	0.800000	0.800000	1.000000	21.000000
	Service dissatisfaction	4	1.000000	0.250000	0.750000	22.250000
	Extra data charges	3	1.000000	0.333333	1.000000	12.000000
	Lack of affordable download/upload speed	3	0.666667	0.333333	0.666667	19.000000
	Lack of self-service on Website	3	0.333333	0.000000	1.000000	24.333333
	Network reliability	3	0.666667	0.000000	1.000000	9.666667
	Poor expertise of online support	3	1.000000	0.666667	1.000000	15.000000
	Poor expertise of phone support	2	0.500000	0.500000	1.000000	8.500000
	Limited range of services	1	0.000000	0.000000	1.000000	13.000000
	Deceased	0	NaN	NaN	NaN	NaN
(33, 35]	Attitude of support person	113	0.628319	0.194690	0.929204	16.681416
	Competitor had better devices	113	0.743363	0.230088	0.893805	18.309735
	Competitor made better offer	84	0.726190	0.297619	0.892857	17.011905
	Don't know	50	0.520000	0.140000	0.900000	16.000000
	Attitude of service provider	35	0.771429	0.314286	0.914286	12.000000
	Competitor offered more data	34	0.764706	0.352941	0.852941	20.882353
	Product dissatisfaction	31	0.709677	0.225806	0.838710	21.935484
	Competitor offered higher download speeds	30	0.566667	0.166667	0.733333	22.666667
	Network reliability	29	0.793103	0.275862	0.896552	16.241379
	Price too high	29	0.724138	0.344828	0.965517	18.482759
	Service dissatisfaction	25	0.600000	0.320000	0.960000	14.200000
	Long distance charges	24	0.500000	0.333333	0.833333	18.791667
	Moved	19	0.736842	0.210526	0.842105	17.157895
	Extra data charges	18	0.666667	0.333333	0.833333	26.277778
	Limited range of services	17	0.647059	0.588235	0.941176	11.117647
	Lack of affordable download/upload speed	13	0.538462	0.384615	0.769231	19.769231
	Lack of self-service on Website	11	0.454545	0.000000	1.000000	13.818182
	Poor expertise of online support	8	0.500000	0.125000	1.000000	17.375000
	Deceased	3	0.666667	0.333333	1.000000	8.333333
	Poor expertise of phone support	3	0.666667	0.000000	0.666667	13.333333
(30, 33]	Competitor made better offer	148	0.736486	0.250000	0.898649	17.500000
	Don't know	17	0.647059	0.176471	0.823529	25.647059
	Competitor had better devices	14	0.642857	0.357143	0.928571	21.500000
	Attitude of support person	10	0.700000	0.300000	1.000000	23.700000
	Product dissatisfaction	5	0.800000	0.200000	0.800000	32.800000
	Attitude of service provider	4	0.750000	0.500000	0.500000	29.250000
	Competitor offered higher download speeds	4	0.500000	0.000000	1.000000	7.750000
	Competitor offered more data	4	0.750000	0.500000	1.000000	19.750000
	Poor expertise of online support	3	1.000000	0.666667	0.666667	30.000000
	Lack of self-service on Website	2	0.500000	0.000000	1.000000	3.500000
	Long distance charges	1	0.000000	0.000000	1.000000	1.000000
	Network reliability	1	0.000000	0.000000	0.000000	54.000000
	Price too high	1	1.000000	0.000000	1.000000	12.000000
	Deceased	0	NaN	NaN	NaN	NaN
	Extra data charges	0	NaN	NaN	NaN	NaN
	Lack of affordable download/upload speed	0	NaN	NaN	NaN	NaN
	Limited range of services	0	NaN	NaN	NaN	NaN
	Moved	0	NaN	NaN	NaN	NaN
	Poor expertise of phone support	0	NaN	NaN	NaN	NaN
	Service dissatisfaction	0	NaN	NaN	NaN	NaN

pd.set_option('display.max_rows', 80)
Churn_Reason = df1[df1['Churn Reason']!='0'].groupby(['geo_bin','Churn Reason']).agg({'Churn Value':'size'})\
                                    .sort_values(ascending=False,\
                                    by=['geo_bin','Churn Value']).rename(columns={'Churn Value':'Churn Reason Count'})

Churn_Reason_Percent = Churn_Reason.groupby(level=0).apply(lambda x:
                                                 (x*100) / x.sum()).rename(columns={'Churn Reason Count':'Churn Reason %'})
Churn_Reason_Percent.groupby('geo_bin',as_index=False).apply(lambda x : x.sort_values(by = 'Churn Reason %', ascending = False).head(5))\
.style.background_gradient(cmap = 'Blues')

			Churn Reason %
	geo_bin	Churn Reason
0	(30, 33]	Competitor made better offer	69.158879
		Don't know	7.943925
		Competitor had better devices	6.542056
		Attitude of support person	4.672897
		Product dissatisfaction	2.336449
1	(33, 35]	Attitude of support person	16.400581
		Competitor had better devices	16.400581
		Competitor made better offer	12.191582
		Don't know	7.256894
		Attitude of service provider	5.079826
2	(35, 37]	Competitor had better devices	20.467836
		Attitude of support person	16.374269
		Attitude of service provider	14.619883
		Competitor made better offer	7.017544
		Don't know	6.432749
3	(37, 42]	Competitor had better devices	18.993711
		Competitor offered more data	9.182390
		Attitude of support person	8.679245
		Competitor made better offer	8.427673
		Competitor offered higher download speeds	7.169811

Analyze the table of churn reasons by binned geographical to make informed business recommendations.

Long-term Goal -

Bin (37, 42] Northern California (includes Bay Area): "Competitor had Better Devices" is the primary churn reason. Recommend investing in improving device quality, in addition to upselling customers to better performance devices as consumers in this area are less price sensitive.

Bin (35, 37] Upper Central California: "Competitor had Better Devices" and "Attitude of Support Person" are the primary reasons for most churn. Recommend retraining the customer service and field agents that are customer facing in this region (i.e. sensitivity training) as well as upselling customers to better performance devices.

Bin (33, 35] Lower Central California (includes LA): "Attitude of Support Person", "Competitor had Better Devices", and "Competitor had Better Offer" were the top churn reasons. Recommend to use the same strategy of re-training customer service and field agents in this region in addition to bundling services and contracts for better deals, and upselling higher performance devices for those that want them.

Bin (30, 33] Southern California (includes SD): "Competitor Made Better Offer" is the primary churn reason. Recommend improving pricing of our services in this territory such as by offering deep discounts on bundling services or larger discounts for longer contracts.

Additional details on analysis

1. Bin (37, 42] Northernmost California includes Bay Area: "Competitor had Better Devices" was main churn reason, accounts for 19% of all churn in area.
2. Bin (35, 37] Central California: The combined churn reasons of "Attitude of Support Person" and "Attitude of Service Provider" account for 31% of the churn for the area.
3. Bin (33, 35] Southern California incl. LA: This areas saw three major patterns in churn reason. The combined churn reasons of "Attitude of Support Person" and "Attitude of Service Provider" account for 21% of the churn, "Competitor had Better Devices" accounted for 16% of the churn and "Competitor Made Better Offer" made up 12% of the churn for the area making this region more complex than the rest.
4. Bin (30, 33] Southernmost California incl. SD: "Competitor Made Better Offer" accounts for 69% of churn in the area. It is important to note that SD has predeominently Fiber Optic customers (74%).

Finding 3: Senior Citizens observe a higher churn rate, however this demographic observes less churn when they are subscribed to tech support. In addition, senior citizens with tech support have a higher average satisfaction score than non-senior citizens who have tech support indicating tech support has higher impact on the senior citizen population.

How did we get to our finding?

We looked for patterns based off of different demographics to check whether there were substantial differences between two populations and found senior citizens were noticeably different in their churn patterns than non-senior citizens. Our original hypothesis was that senior citizens were less likely to churn given the barriers and logistical efforts required to change service providers, however, we found this was not the case below.

df1.head()

	Senior Citizen	Partner	Dependents	Tenure	Phone Service	Multiple Lines	Online Security	Online Backup	Device Protection	Tech Support	Streaming TV	Streaming Movies	Paperless Billing	Payment Method	Monthly Charges	Total Charges	Gender	Age	Under 30	Number of Dependents	City	Zip Code	Latitude	Longitude	Population	Total Revenue	Satisfaction Score	Churn Value	Churn Score	Churn Category	Churn Reason	churn_rate	Internet Service_0	Internet Service_DSL	Internet Service_Fiber optic	Contract_Month-to-month	Contract_One year	Contract_Two year	geo_bin
0	0	1	0	1	0	0	0	1	0	0	0	0	1	Electronic check	29.85	29.85	Female	36	0	0	Los Angeles	90001	33.973616	-118.249020	54492	29.85	3	0	27	0	0	0.25	0	1	0	1	0	0	(33, 35]
1	0	0	0	34	1	0	1	0	1	0	0	0	0	Mailed check	56.95	1889.50	Male	46	0	0	Los Angeles	90002	33.949255	-118.246978	44586	2470.56	3	0	61	0	0	0.00	0	1	0	0	1	0	(33, 35]
2	0	0	0	2	1	0	1	1	0	0	0	0	1	Mailed check	53.85	108.15	Male	37	0	0	Los Angeles	90003	33.964131	-118.272783	58198	129.09	1	1	86	Competitor	Competitor made better offer	0.20	0	1	0	1	0	0	(33, 35]
3	0	0	0	45	0	0	1	0	1	1	0	0	0	Bank transfer (automatic)	42.30	1840.75	Male	53	0	0	Los Angeles	90004	34.076259	-118.310715	67852	1840.75	3	0	66	0	0	0.40	0	1	0	0	1	0	(33, 35]
4	0	0	0	2	1	0	0	0	0	0	0	0	1	Electronic check	70.70	151.65	Female	19	1	2	Los Angeles	90005	34.059281	-118.307420	43019	169.89	2	1	67	Other	Moved	0.50	0	0	1	1	0	0	(33, 35]

df5 = df1.copy()

#Renaming columns
df5.rename(columns=\
        {'Senior Citizen':'Senior_Citizen', 'Phone Service':'Phone_Service','Multiple Lines':'Multiple_Lines',\
        'Online Security':'Online_Security','Online Backup':'Online_Backup','Device Protection':'Device_Protection',\
        'Tech Support':'Tech_Support','Streaming TV':'Streaming_TV','Streaming Movies':'Streaming_Movies',\
        'Paperless Billing':'Paperless_Billing','Payment Method':'Payment_Method','Monthly Charges':'Monthly_Charges',\
        'Total Charges':'Total_Charges','Satisfaction Score':'Satisfaction_Score','Churn Value':'Churn_Value',\
        'Churn Score':'Churn_Score','Churn Category':'Churn_Category','Churn Reason':'Churn_Reason'},inplace=True)

Senior citizen population is more likely to churn than non-senior citizens (i.e. 41.68% vs 23.60%).

senior_citizen_churn = df5.groupby(['Senior_Citizen','Churn_Value']).agg({'Churn_Value':'count'})

Senior_Churn_Percents = senior_citizen_churn.groupby(level=0).apply(lambda x:
                                                 (x*100) / x.sum()).rename(columns={'Churn_Value':'Churn %'})

df5.groupby(['Senior_Citizen','Churn_Value']).agg({'Churn_Value':'count'})\
.rename(columns={'Churn_Value':'Churn Count'})
Senior_Churn_Percents

		Churn Count
Senior_Citizen	Churn_Value
0	0	4508
	1	1393
1	0	666
	1	476

		Churn %
Senior_Citizen	Churn_Value
0	0	76.393832
	1	23.606168
1	0	58.318739
	1	41.681261

sns.countplot(x='Churn_Value', hue='Senior_Citizen', data=df5)

<AxesSubplot:xlabel='Churn_Value', ylabel='count'>

However, Senior citizens who have tech support are less likely to churn versus those who do not (i.e. 18.30% vs 4.46%).

tech_support_senior = df5[df5.Senior_Citizen == 1].groupby(['Senior_Citizen','Churn_Value','Tech_Support'])\
.agg({'Churn_Value':'count'})

tech_support_senior_percents = tech_support_senior.groupby(level=0).apply(lambda x:
                                                 (x*100) / x.sum()).rename(columns={'Churn_Value':'Churn %'})

df5[df5.Senior_Citizen == 1].groupby(['Senior_Citizen','Churn_Value','Tech_Support'])\
.agg({'Churn_Value':'count'})
tech_support_senior_percents

			Churn_Value
Senior_Citizen	Churn_Value	Tech_Support
1	0	0	457
		1	209
	1	0	425
		1	51

			Churn %
Senior_Citizen	Churn_Value	Tech_Support
1	0	0	40.017513
		1	18.301226
	1	0	37.215412
		1	4.465849

sns.countplot(x='Churn_Value', hue='Tech_Support', data=df5[df5.Senior_Citizen==1])

<AxesSubplot:xlabel='Churn_Value', ylabel='count'>

Non Senior Citizens who don't have tech support observe half the churn rate of Senior Citizens who don't have tech support (i.e. 19.217% vs 37.215%)

tech_support_nonsenior = df5[df5.Senior_Citizen == 0].groupby(['Senior_Citizen','Churn_Value','Tech_Support'])\
.agg({'Churn_Value':'count'})

tech_support_nonsenior_percents = tech_support_nonsenior.groupby(level=0).apply(lambda x:
                                                 (x*100) / x.sum()).rename(columns={'Churn_Value':'Churn %'})

df5[df5.Senior_Citizen == 0].groupby(['Senior_Citizen','Churn_Value','Tech_Support'])\
.agg({'Churn_Value':'count'})
tech_support_nonsenior_percents

			Churn_Value
Senior_Citizen	Churn_Value	Tech_Support
0	0	0	2983
		1	1525
	1	0	1134
		1	259

			Churn %
Senior_Citizen	Churn_Value	Tech_Support
0	0	0	50.550754
		1	25.843077
	1	0	19.217082
		1	4.389087

Senior citizens who have tech support have a higher average satisfaction rate than not (3.303 vs 2.815).

df5.Satisfaction_Score[(df5.Senior_Citizen == 1) & (df5.Tech_Support == 1)].mean()
df5.Satisfaction_Score[(df5.Senior_Citizen == 1) & (df5.Tech_Support == 0)].mean()

3.3038461538461537

2.815192743764172

Non-senior citizens who don't have tech support have a higher average satisfaction rate than senior citizens who don't (3.29 vs 2.815).

df5.Satisfaction_Score[(df5.Senior_Citizen == 0) & (df5.Tech_Support == 1)].mean()
df5.Satisfaction_Score[(df5.Senior_Citizen == 0) & (df5.Tech_Support == 0)].mean()

3.344170403587444

3.290259897983969

Senior citizens also have a higher concentration of fiber optic subscriptions than non-senior citizens (82.56% vs 64.89%). As we have previously mentioned, the fiber optic product is one of the primary reasons for churn.

Fiber_optic = df5[df5.Churn_Value==1].groupby(['Senior_Citizen','Internet Service_Fiber optic'])\
                .agg({'Internet Service_Fiber optic':'count'})

Fiber_optic_percents = Fiber_optic.groupby(level=0).apply(lambda x:
                            (x*100) / x.sum()).rename(columns={'Internet Service_Fiber optic':'Internet Service %'})

Fiber_optic
Fiber_optic_percents.style.background_gradient(cmap = 'Blues')

		Internet Service_Fiber optic
Senior_Citizen	Internet Service_Fiber optic
0	0	489
	1	904
1	0	83
	1	393

		Internet Service %
Senior_Citizen	Internet Service_Fiber optic
0	0	35.104092
	1	64.895908
1	0	17.436975
	1	82.563025

Short-term Goal - Given this nuance in our senior citizen population, we recommend creating a new internet plan tailored towards seniors by automatically including free tech support along with their chosen internet service or bundling a tech support add-on at a significant discount. Senior citizens who stayed make up 17% of total current customers monthly revenue and senior citizens who churned make up 28% of the total churned customers monthly forgoed revenue hence improving retention in this demographic is important to overall company financial performance.

</div>

!jupyter nbconvert --to html Telco_Churn_FinalVersion.ipynb

[NbConvertApp] WARNING | pattern 'Telco_Churn_FinalVersion.ipynb' matched no files
This application is used to convert notebook files (*.ipynb) to various other
formats.

WARNING: THE COMMANDLINE INTERFACE MAY CHANGE IN FUTURE RELEASES.

Options
=======
The options below are convenience aliases to configurable class-options,
as listed in the "Equivalent to" description-line of the aliases.
To see all configurable class-options for some <cmd>, use:
    <cmd> --help-all

--debug
    set log level to logging.DEBUG (maximize logging output)
    Equivalent to: [--Application.log_level=10]
--generate-config
    generate default config file
    Equivalent to: [--JupyterApp.generate_config=True]
-y
    Answer yes to any questions instead of prompting.
    Equivalent to: [--JupyterApp.answer_yes=True]
--execute
    Execute the notebook prior to export.
    Equivalent to: [--ExecutePreprocessor.enabled=True]
--allow-errors
    Continue notebook execution even if one of the cells throws an error and include the error message in the cell output (the default behaviour is to abort conversion). This flag is only relevant if '--execute' was specified, too.
    Equivalent to: [--ExecutePreprocessor.allow_errors=True]
--stdin
    read a single notebook file from stdin. Write the resulting notebook with default basename 'notebook.*'
    Equivalent to: [--NbConvertApp.from_stdin=True]
--stdout
    Write notebook output to stdout instead of files.
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--inplace
    Run nbconvert in place, overwriting the existing notebook (only 
    relevant when converting to notebook format)
    Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory=]
--clear-output
    Clear output of current file and save in place, 
    overwriting the existing notebook.
    Equivalent to: [--NbConvertApp.use_output_suffix=False --NbConvertApp.export_format=notebook --FilesWriter.build_directory= --ClearOutputPreprocessor.enabled=True]
--no-prompt
    Exclude input and output prompts from converted document.
    Equivalent to: [--TemplateExporter.exclude_input_prompt=True --TemplateExporter.exclude_output_prompt=True]
--no-input
    Exclude input cells and output prompts from converted document. 
    This mode is ideal for generating code-free reports.
    Equivalent to: [--TemplateExporter.exclude_output_prompt=True --TemplateExporter.exclude_input=True]
--allow-chromium-download
    Whether to allow downloading chromium if no suitable version is found on the system.
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--log-level=<Enum>
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    one notebook at a time.
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    The URL prefix for reveal.js (version 3.x). This defaults to the reveal CDN,
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    slideshow) for more details.
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--nbformat=<Enum>
    The nbformat version to write. Use this to downgrade notebooks.
    Choices: any of [1, 2, 3, 4]
    Default: 4
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Examples
--------

    The simplest way to use nbconvert is
    
    > jupyter nbconvert mynotebook.ipynb --to html
    
    Options include ['asciidoc', 'custom', 'html', 'latex', 'markdown', 'notebook', 'pdf', 'python', 'rst', 'script', 'slides', 'webpdf'].
    
    > jupyter nbconvert --to latex mynotebook.ipynb
    
    Both HTML and LaTeX support multiple output templates. LaTeX includes
    'base', 'article' and 'report'.  HTML includes 'basic' and 'full'. You
    can specify the flavor of the format used.
    
    > jupyter nbconvert --to html --template lab mynotebook.ipynb
    
    You can also pipe the output to stdout, rather than a file
    
    > jupyter nbconvert mynotebook.ipynb --stdout
    
    PDF is generated via latex
    
    > jupyter nbconvert mynotebook.ipynb --to pdf
    
    You can get (and serve) a Reveal.js-powered slideshow
    
    > jupyter nbconvert myslides.ipynb --to slides --post serve
    
    Multiple notebooks can be given at the command line in a couple of 
    different ways:
    
    > jupyter nbconvert notebook*.ipynb
    > jupyter nbconvert notebook1.ipynb notebook2.ipynb
    
    or you can specify the notebooks list in a config file, containing::
    
        c.NbConvertApp.notebooks = ["my_notebook.ipynb"]
    
    > jupyter nbconvert --config mycfg.py

To see all available configurables, use `--help-all`.